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The Li-Ion battery state-of-charge estimation is an essential task in a continuous dynamic automotive industry for large-scale and successful marketing of hybrid electric vehicles. Also, the state-of-charge of any rechargeable battery, regardless of its chemistry, is an essential condition parameter for battery management systems of hybrid electric...
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The pursuit for safer and more efficient vehicles is a challenging task that has attracted many of the brightest engineers and scientists. With more sensors and active controls available on ground vehicles, they have become safer than ever. Recently, driver assistance functions are widely deployed, and the prospect of driverless operations is close...
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... Zhou et al. [45] tested the estimation of SoC by EKF with stress tests using conduction cycles. [46] compared the EKF with the performance of the proportional integral observer method and concluded that EKF performed better. ...
Recently, electric vehicles have gained enormous popularity due to their performance and efficiency. The investment in developing this new technology is justified by the increased awareness of the environmental impacts caused by combustion vehicles, such as greenhouse gas emissions, which have contributed to global warming and the depletion of oil reserves that are not renewable energy sources. Lithium-ion batteries are the most promising for electric vehicle (EV) applications. They have been widely used for their advantages, such as high energy density, many cycles, and low self-discharge. This work extensively investigates the main methods of estimating the state of charge (SoC) obtained through a literature review. A total of 109 relevant articles were found using the prism method. Some basic concepts of the state of health (SoH); a battery management system (BMS); and some models that can perform SoC estimation are presented. Challenges encountered in this task are discussed, such as the nonlinear characteristics of lithium-ion batteries that must be considered in the algorithms applied to the BMS. Thus, the set of concepts examined in this review supports the need to evolve the devices and develop new methods for estimating the SoC, which is increasingly more accurate and faster. This review shows that these tools tend to be continuously more dependent on artificial intelligence methods, especially hybrid algorithms, which require less training time and low computational cost, delivering real-time information to embedded systems.
... The BMS is also integrated into the HEV/EV architecture and monitors internal battery parameters at the cell and battery pack levels. Battery state of charge (SOC) is a critical internal parameter that must be monitored due to its strong impact on the battery health and lifetime [3][4][5][6][7][8][9][10]. This parameter is defined as remaining battery capacity during the time period when the battery discharges. ...
... This parameter is defined as remaining battery capacity during the time period when the battery discharges. The main drawback is that LIB SOC cannot be measured directly due to the lack of an accurate measurement sensor; thus, a proper estimation technique is required to prevent dangerous situations and battery performance degradation [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. The majority of Li-ion battery SOC estimation algorithms are modelbased, among them the most popular extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter (PF), and adaptive nonlinear observers (ANOE) are intensively used and well documented in the literature of the field [4][5][6][7][8][9][10][11][12][13][14][15][16][17]. ...
... The main drawback is that LIB SOC cannot be measured directly due to the lack of an accurate measurement sensor; thus, a proper estimation technique is required to prevent dangerous situations and battery performance degradation [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. The majority of Li-ion battery SOC estimation algorithms are modelbased, among them the most popular extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter (PF), and adaptive nonlinear observers (ANOE) are intensively used and well documented in the literature of the field [4][5][6][7][8][9][10][11][12][13][14][15][16][17]. The performance of these SOC estimators and terminal voltage predictors, such as estimation accuracy, convergence, and robustness to changes in the battery model parameters and initial "guess value" for battery SOC, as well as to the real-world driving conditions, is limited by many factors, such as the type of the application, the aggressivity of the battery model nonlinearity, uncertainties and unmodeled battery dynamics, battery model accuracy, and the difficulties experienced to find the best values for tuning parameters, among others. ...
The main objective of this research paper was to develop two intelligent state estimators using shallow neural network (SNN) and NARX architectures from a large class of deep learning models. This research developed a new modelling design approach, namely, an improved hybrid adaptive neural fuzzy inference system (ANFIS) battery model, which is simple, accurate, practical, and well suited for real-time implementations in HEV/EV applications, with this being one of the main contributions of this research. On the basis of this model, we built four state of charge (SOC) estimators of high accuracy, assessed by a percentage error of less than 0.5% in a steady state compared to the 2% reported in the literature in the field. Moreover, these estimators excelled by their robustness to changes in the model parameters values and the initial “guess value” of SOC from 80–90% to 30–40%, performing in the harsh and aggressive realistic conditions of the real world, simulated by three famous driving cycle procedure tests, namely, two European standards, WLTP and NEDC, and an EPA American standard, FTP-75. Furthermore, a mean square error (MSE) of 7.97 × 10−11 for the SOC estimation of the NARX SNN SOC estimator and 5.43 × 10−6 for voltage prediction outperformed the traditional SOC estimators. Their effectiveness was proven by the performance comparison with a traditional extended Kalman filter (EKF) and adaptive nonlinear observer (ANOE) state estimators through extensive MATLAB simulations that reveal a slight superiority of the supervised learning algorithms by accuracy, online real-time implementation capability, in order to solve an extensive palette of HEV/EV applications.
... Moreover, a proposed model can be easily linked to other circuit blocks for comparison between real estimation and simulated data. Then, an extended Kalman filter algorithm (EKF) [8][9][10] was developed to enhance SOC estimation. Therefore, the coulomb counting method [11][12][13] has been used as a reference for SOC estimation. ...
This paper focuses on the state of charge estimation (SOC) for battery Li-ion. By modeling a battery based on the equivalent circuit model, the extended Kalman filter approach can be applied to estimate the battery SOC. An electrical battery model is developed in Matlab, Where the structure of the model is detailed by equations and blocks. The battery model has been validated from the experiment results. The comparison shows a good agreement in predicting the voltage, SOC estimation and the model performs better in SOC estimation.
... Moreover, a proposed model can be easily linked to other circuit blocks for comparison between real estimation and simulated data. Then, an extended Kalman filter algorithm (EKF) [8][9][10] was developed to enhance SOC estimation. Therefore, the coulomb counting method [11][12][13] has been used as a reference for SOC estimation. ...
This paper focuses on the state of charge estimation (SOC) for battery Li-ion. By modeling a battery based on the equivalent circuit model, the extended Kalman filter approach can be applied to estimate the battery SOC. An electrical battery model is developed in Matlab, Where the structure of the model is detailed by equations and blocks. The battery model has been validated from the experiment results. The comparison shows a good agreement in predicting the voltage, SOC estimation and the model performs better in SOC estimation.
... The first constraint is specifically used for non-thermal loads, where S a is the minimum level of power consumption extracted as in [34]. A logarithmic function is used to ensure minimum device performance in the limit as such a function saturates [35]. The second constraint is used for the thermal system and depends on environment and appliance energy dissipation. ...
The Energy-efficiency of demand management technologies and customer’s experience have emerged as important issues as consumers began to heavily adopt these technologies. In this context, where the electrical load imposed on the smart grid by residential users needs to be optimized, it can be better managed when customer’s comfort parameters are used, such as thermal comfort and preferred appliance usage time interval. In this paper a multi-layer architecture is proposed that uses a multi-objective optimization model at the energy consumption level to take consumer comfort and experience into consideration. The paper shows how our proposed Clustered Sequential Management (CSM) approach could improve consumer comfort via appliance use scheduling. To quantify thermal comfort, we use thermodynamic solutions for a Heating Ventilation and Air Conditioner (HVAC) system and then apply our scheduling model to find the best time slot for such thermal loads, linking consumer experience to power consumption. In addition to thermal loads, we also include non-thermal loads in the cost minimization and the enhanced consumer experience. In this hierarchal algorithm, we classified appliances by their load profile including degrees of freedom for consumer appliance prioritization. Finally, we scheduled consumption within a Time of Use (ToU) pricing model. In this model, we used Mixed Integer Linear Programming (MILP) and Linear Programming (LP) optimization for different categories with different constraints for various loads. We eliminate the customer’s inconvenience on thermal load considering ASHRAE standard, increase the satisfaction on EV optimal chagrining constrained by minimum cost and achieve the preferred usage time for the non-interruptible deferrable loads. The results show that our model is typically able to achieve cost minimization almost equal to 13% and Peak-to-Average Ratios (PAR) reduction with almost 45%.
... In the absence of a measurement sensor, the SOC cannot be measured directly, thus its estimation using Kalman filter estimation techniques is required [17][18][19][20][21][22][23][24][25][26][27][28][29], a topic that is detailed in Part 2 [30]. Furthermore, an accurate Li-ion battery model is essential in SOC estimation of the modelbased BMS in electric vehicles (EVs)/HEVs. ...
... Therefore, these issues require a comprehensive analysis to consider their impact on solving the correct and accurate battery SOC estimation. At the end of this section, a brief review is given of some linear and non-linear analytic battery models of different chemistries reported in the literature well-suited for "battery design, performance estimation, prediction for real-time power management, and circuit simulation", such as is done in [17,18,28]. These models can be categorized into five categories: electrochemical models, computational intelligence-based models, analytical models, stochastic models, and electrical circuit models, as is mentioned [17]. ...
... At the end of this section, a brief review is given of some linear and non-linear analytic battery models of different chemistries reported in the literature well-suited for "battery design, performance estimation, prediction for real-time power management, and circuit simulation", such as is done in [17,18,28]. These models can be categorized into five categories: electrochemical models, computational intelligence-based models, analytical models, stochastic models, and electrical circuit models, as is mentioned [17]. ...
Battery state of charge (SOC) accuracy plays a vital role in a hybrid electric vehicle (HEV), as it ensures battery safety in a harsh operating environment, prolongs life, lowers the cost of energy consumption, and improves driving mileage. Therefore, accurate SOC battery estimation is the central idea of the approach in this research, which is of great interest to readers and increases the value of its application. Moreover, an accurate SOC battery estimate relies on the accuracy of the battery model parameters and its capacity. Thus, the purpose of this paper is to design, implement and analyze the SOC estimation accuracy of two battery models, which capture the dynamics of a rechargeable SAFT Li-ion battery. The first is a resistor capacitor (RC) equivalent circuit model, and the second is a generic Simscape model. The model validation is based on the generation and evaluation of the SOC residual error. The SOC reference value required for the calculation of residual errors is the value estimated by an ADVISOR 3.2 simulator, one of the software tools most used in automotive applications. Both battery models are of real interest as a valuable support for SOC battery estimation by using three model based Kalman state estimators developed in Part 2. MATLAB simulations results prove the effectiveness of both models and reveal an excellent accuracy.
... In recent years, the lithium-ion battery has proven to be an ideal safety battery for hybrid electric vehicles, with high discharge power, environmental protection, low pollution, and long life [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Some details about its features, modelling, and hybrid combinations with different power sources in a fuel cell electric vehicle (FCEV) and power distribution controlled and optimized by an energy management system (EMS) are shown in Part 1 [20]. ...
... Typically, the Kalman filter SOC estimators are model based, so both battery models-the linear RC equivalent electrical circuit (ECM) and the nonlinear Simscape generic model developed and analyzed in Part 1 [20] are beneficial for designing and implementing a high accuracy SOC estimator [1][2][3][4][5][6][7][8][9][10][12][13][14][15][16][17][18][19]. For better documentation and information for the reader, the diagrams of both models represented in Part 1 [20] are taken over and repeated in Appendix A1, Figure A1a-c. ...
... However, there are situations when some Li-ion battery models have a dynamic that is "extremely nonlinear" and therefore "the linearization error may occur due to the lack of precision in the extension of the first series Taylor series in extremely nonlinear conditions" [5]. The simplicity of the SOC EKF estimator design and real-time MATLAB implementation is among two main features that motivated many researchers to apply it to a variety of Li-ion battery models, as in [2,3,[6][7][8][9]. A new state of the art analysis on Li-ion BMSs is presented in [12], which includes a brief overview presentation of the most common adaptive filtration techniques for SOC estimation reported in the literature. ...
The purpose of this paper is to analyze the accuracy of three state of charge (SOC) estimators of a rechargeable Li-ion SAFT battery based on two accurate Li-ion battery models, namely a linear RC equivalent electrical circuit (ECM) and a nonlinear Simscape generic model, developed in Part 1. The battery SOC of both Li-ion battery models is estimated using a linearized adaptive extended Kalman filter (AEKF), a nonlinear adaptive unscented Kalman filter (AUKF) and a nonlinear and non-Gaussian particle filter estimator (PFE). The result of MATLAB simulations shows the efficiency of all three SOC estimators, especially AEKF, followed in order of decreasing performance by AUKF and PFE. Besides, this result reveals a slight superiority of the SOC estimation accuracy when using the Simscape model for SOC estimator design. Overall, the performance of all three SOC estimators in terms of accuracy, convergence of response speed and robustness is excellent and is comparable to state of the art SOC estimation methods.
... Secondly, the Coulombs Counting method that relies on the current integration depending on a controlled sensor, however, a regular calibration should be done to avoid any error [32][33][34][35][36][37]. The last one is the machine learning method, which is based on the reliability of the collected data and includes the following: artificial intelligent [38][39][40], the support vector machines algorithm (SVM) [41,42] and Kalman filter family methods that rely on the state-space model, yet, the machine learning method has a poor performance in transients [43][44][45][46][47][48][49]. SOC with a low percentage of error is required to optimize the energy loss, the time interval required to charge the battery, safety usage, and battery management. ...
Fast charging of electric-vehicles is one of the paramount challenge in solar smart cities. This paper investigates intelligent optimization methodology to improvise the existing approaches in order to speed up the charging process whilst reducing the energy consumption without relapsing the battery performance in the light of the outrageous demand for lithium-ion battery in the electric vehicles (EVs). Two fitness functions are combined as the targeted objective function: energy losses (EL) and charging interval time (CIT). An intelligent optimization methodology based on Cuckoo Optimization Algorithm (COA) is implemented to the objective function for improving the charging performance of the lithium-ion battery. COA is applied through two main techniques: Hierarchical technique (HT) and Conditional random technique (CRT). Experimental results show that the proposed techniques permit a full charging capacity of the polymer lithium-ion battery (0 to 100% SOC) within 91 mins. Compared with constant current-constant voltage (CCCV) technique, an improvement in the efficiency of 8% and 14.1% was obtained by Hierarchical technique (HT) and Conditional random technique (CRT) respectively, in addition to a reduction in energy losses of 7.783% and 10.408% respectively and a reduction in charging interval time of 18.1% and 22.45% respectively. Experimental and theoretical analyses are performed and are in good agreement on the polymer lithium-ion battery fast charging method.
... The Coulomb Counting method can be improved by considering the Coulombic efficiency (Ah) at different temperature and charge rates. The estimation of SoC using the modified CC Technique is given in Eqn. 2 ∫ (2) η eq and C a representing the Equivalent Coulombic Efficiency (ECE); Among the different battery chemistries, Li-ion batteries offer the highest Coulombic efficiency in the normal SoC region (exceeds 99%) Tudoroiu et al., 2018). But the estimation of Coulomb efficiency is difficult task as it requires highly accurate equipment. ...
Vehicle electrification can effectively mitigate oil crisis and environmental pollution. In an Electric Vehicle (EV), several cells connected in series/parallel topologies, powering the EV for the maximum range of 50-500km. Consequently, Battery Management System (BMS) is necessary to manage the battery pack to ensure safe and proper operations of EV.With increase of cycle numbers of Li-ion batteries, the electrode materials gradually become inactive, leading to the performance degradation of the battery. The battery State of Health (SoH) is an estimation to evaluate the battery capability status, by which the battery related inner parameters including State of Charge (SoC) and remaining driving range can be accessed with higher precision. Accurate SoC estimations have always been a critical and important concern in the design of BMS. To design an efficient BMS, it is important to precisely acquire battery pack voltage, cell voltage, current and temperature of the EV battery.This paper proposed an efficient Battery Management System to estimate SoC and SoH accurately using LabVIEW based c-RIO Data Acquisition System (DAQ) with Support Vector Machine (SVM). In this work, the battery parameters have been precisely acquired through c-RIO DAQ based measurement and control system.The system swiftly process the acquired battery parameters and produced the Open Circuit Voltage (OCV), Current, Thermal parameters. These outputs of c-RIO DAQ were applied to the SVM module to estimate SoC and SoH of the battery pack precisely. The result ensured that the proposed system efficiently estimate the SoC and SoH over the coulomb counting method.
... Unfortunately, these parameters are highly sensitive to the accuracy of the measuring equipment and different operating conditions. An ECM-based method is a method of estimating the capacity or impedance using an estimation algorithm based on an ECM, such as the extended Kalman filter (EKF) and observer algorithm [8][9][10][11][12]. An ECM is a physical representation of the electrochemical properties of a battery [13]. ...
To recycle retired series/parallel battery packs, it is necessary to know their state-of-health (SOH) correctly. Unfortunately, voltage imbalances between the cells occur repeatedly during discharging/charging. The voltage ranges for the discharge/charge of a retired series/parallel battery pack are reduced owing to the voltage imbalances between the cells. To determine the accurate SOH of a retired series/parallel battery pack, it is necessary to calculate the total discharge capacity using fully discharging/charging tests. However, a fully discharging/charging test is impossible owing to the reduced voltage range. The SOH of a retired series/parallel battery pack with a voltage imbalance should be estimated within the reduced discharging/charging voltage range. This paper presents a regression analysis of the peak point in the incremental capacity (IC) curve from the fresh state to a 100-cycle aging state. Moreover, the SOH of the considered retired series/parallel battery pack was estimated using a regression analysis model. The error in the SOHs of the retired series/parallel battery pack and linear regression analysis model was within 1%, and hence a good accuracy is achieved.
